The chest x-ray is a useful diagnostic tool that assists in detecting a number of patient conditions and for imaging a range of skeletal and organ structures. In clinical applications such as in the Intensive Care Unit (ICU), the chest x-ray can have particular value for indicating pneumothorax as well as for tube/line positioning, and other clinical conditions. Pneumothorax is a condition caused by an accumulation of air or gas in the pleural cavity, which can occur as a result of disease or injury. Radiographic detection of pneumothorax is commonly based on observing a subtle, fine curved-line pattern in the apical lung region, a dark pleural air space against the chest wall due to increased transparency, and a lack of lung structure between the rib cage and the pneumothorax pattern. The radiologist can recognize the outer lung membrane in shadowy fashion in the chest image, as well as blood vessels that proceed from the middle of the lung toward the edge and end at the lung membrane.
Although pneumothoraces are clinically important abnormalities, it is often difficult to detect them in the radiographic image. Some of the problems that complicate pneumothorax detection are due to the location of this condition, since there can be some overlap between the pneumothorax region and nearby ribs and clavicle. Edge detection and rib removal routines, which can be helpful for allowing improved visibility of some types of conditions, can be detrimental to display of pneumothorax in many cases, since such routines can often mistake the boundary along which pneumothorax is detected as a type of edge and, deriving this inaccurate information from the image, incorrectly apply edge suppression. This may result in actually reducing the visibility of the pneumothorax condition. Because of this, and as a result of similar problems, it can be particularly difficult to detect pneumothorax using a computer-aided detection (CAD) system.
The advent of mobile X-ray imaging systems that can be wheeled up to the patient's bedside, such as in an ICU facility, makes it desirable to be able to enhance various conditions related to tissue such as pneumothorax or lung nodules, as well as to enhance tube and line contours in an obtained image. Where a condition such as pneumothorax is identified it can be further helpful, where possible, to report this condition automatically using image analysis utilities. The capability to provide this function would be valuable for improving patient care and response to patient condition. However, due to the complexity of the image analysis problem and due to the relative subtlety of its visual indications, pneumothorax detection and enhancement continue to present a problem that is elusive for conventional image processing and analysis approaches.
One approach to the particular problem of pneumothorax detection is presented in U.S. Pat. No. 5,668,888, entitled “Method and System for Automatic Detection of Ribs and Pneumothorax in Digital Chest Radiographs” (Doi).
Because of the diverse types of tissues and structures involved, the chest x-ray presents a number of challenges to conventional techniques for image enhancement. One set of problems for improving the detectability of pneumothorax as well as for detecting lung nodules or line and tube placement relates to imaging differences between lung tissue and tissues in the abdominal region. In many chest x-rays the abdominal region appears to be highly uniform when compared against other areas of the image. Because of this, image enhancement techniques that may work well over non-uniform regions of the image can tend to generate artifacts when applied over the more uniform region or when applied along the boundaries of the more uniform area.
Among contrast enhancement techniques of considerable promise are pre-processing techniques that perform histogram equalization (HE). Using such methods, a histogram is generated for the image, then a transform is applied in order to re-allocate histogram values to a more suitable range of values. While this works well with some types of images, however, histogram equalization is indiscriminate and can actually enhance the visibility of noise as well as the intended signal. This effect can be particularly noticeable in background areas, but also affects areas of diagnostic interest in the radiographic image.
Contrast Limited Adaptive Histogram Equalization (CLAHE) is an improvement upon conventional histogram equalization methods that uses the local neighborhood of the image pixel in order to enhance image contrast. In CLAHE processing, the image is effectively tiled into local regions. An adaptive contrast enhancement is then applied within each region. This involves generating and processing a local histogram for each region, then equalizing values within the region from a narrower range to a broader range of values. An interpolation process then smoothes out discontinuities in appearance between adjacent tiles.
CLAHE processing allows adjustment of variables such as histogram clipping, which effectively adjusts the contrast characteristic, and tile sizing, so that a suitably sized region is used for histogram equalization. When applied with a small amount of clipping and an appropriate tiling scheme, CLAHE processing can improve image contrast to some degree without over-enhancing noise content or introducing artifacts to the processed image. However, increased clipping may be needed in order to boost contrast when using CLAHE.
One artifact that often results from CLAHE pre-processing is ripple, a low-frequency imaging effect that is most visible over uniform areas of the image (such as the abdominal region), but also affects less uniform portions of the image. Where ripple occurs, there can be difficulty in detecting the edges of structures, such as those that show a pneumothorax condition, for example. Ripple can be reduced somewhat, by smoothing the image data. However, this type of solution can compromise image quality and reduce contrast, losing information and effectively defeating the processing for contrast enhancement that produced ripple in the first place. This ripple artifact is also referred to as a “ring artifact” or “boundary artifact” and is the artifact in homogeneous areas noted by Zimmerman et al, in an article entitled “A Psychophysical Comparison of Two Methods for Adaptive Histograms Equalization”, Journal of Digital Imaging, Vol. 2, No. 2, May, 1989, pp. 82-91 and by Rehm et al. in an article entitled “Artifact Suppression in Digital Chest Radiographs Enhanced with Adaptive Histogram Equalization”, SPIE Vol. 1092 Medical Imaging III: Image Processing (1989) pp. 290-300.
In some cases, the ripple artifact is only observed along the edge of the heart, mediastinum, rib cage and diaphragm. However, when a combination of a smaller tile size and/or higher clipping value is used, these boundary artifacts extend beyond these boundaries into relative uniform areas and become more obvious ripple patterns, such as those that appear in FIG. 1, which shows a chest X-ray having ripple artifacts.
Although these artifacts seem to appear only along areas close to boundaries, such as from low density lung area to high-density anatomy areas, the root cause of these artifacts appears to be due to an over-enhancement of regions that have relative uniform density. Various methods to remove or reduce these boundary artifacts have been proposed. Rehm et al., in the article noted earlier, proposed to reduce these artifacts by subtracting large structure background content to remove the density shift or high contrast at the boundary.
Other, more complex solutions for reducing ripple in the processed image include processes that adjust or modify the CLAHE processing scheme for individual tiles. However, if proper care is not given in selecting the clipping level or other CLAHE related variables, the difference in local contrast enhancement using such processing from one tile to another tile may have negative effects on image uniformity in terms of detail contrast. Achieving differences in detail contrast enhancement, such as blurring and losing contrast over areas with a lower detail contrast enhancement when compared to areas with higher contrast enhancement, can have negative effects. While this processing may reduce ripple somewhat, its results often fall short of diagnostic quality if a consistent detail contrast in an image is required.
Thus, it can be appreciated that there is a need for enhancement techniques for chest x-rays and other radiographic images, where such techniques enhance the visualization of both diagnostic and clinical conditions, without increasing noise content or introducing image artifacts, and offer improved robustness and accuracy over earlier methods.